[Question]: Reproduce LLMLingua-2 on the LongBench SingleDoc dataset

Describe the issue

We referred to your code https://github.com/microsoft/LLMLingua/blob/main/experiments/llmlingua2/evaluation/compress.py, https://github.com/microsoft/LLMLingua/blob/main/experiments/llmlingua2/evaluation/eval_longbench.py

target token: 2000
compresse model: llmlingua-2-bert-base-multilingual-cased-meetingbank
llm model: Mistral-7B-Instruct-v0.1 (from https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1/tree/main)
LongBench singledoc tasks: qasper, multifieldqa_en, narrativeqa
Hardware platform: 1 Nvidia A100-80GB

The result is different from the conclusions in the paper (Table 4, LLMLingua-2-small, LongBench-SingleDoc , 2000-token cons.)
The compressed prompt evaluation score is:
{'qasper': 32.27, 'multifieldqa_en': 33.04, 'narrativeqa': 8.84}
average score 24.7 (25.3 in paper)

The uncompressed prompt evaluation score is:
{"multifieldqa_en": 37.07, "qasper": 33.83, "narrativeqa": 19.89}
30.3 (24.5 in paper)

What's the experiment settings in the paper and what makes the difference in the evaluation result. Thank you for your reply